Abstract
The introduction of data-related information technologies in manufacturing allows to capture large volumes of data from the sensors monitoring the production processes and different alarms associated to them. An early prediction of those alarms can bring several benefits to manufacturing companies such as predictive maintenance of the equipment, or production optimization. This paper introduces a new system that allows to anticipate the activation of several alarms and thus, warns the operators in the plants about situations that could hamper the machines operation or stop the production process. The system follows a two-stage forecaster–analyzer approach on which first, a long short-term memory recurrent neural network based forecaster predicts the future sensor’s measurements and then, distinct analyzers based on residual neural networks determine whether the predicted measurements will trigger an alarm or not. The system supports some features that make it particularly suitable for smart manufacturing scenarios: on the one hand, the forecaster is able to predict the future measurements of different types of time-series data captured by various sensors in non-stationary environments with dynamically changing processes. On the other hand, the analyzers are able to detect alarms that can be modeled with simple rules based on the activation condition, and also more complex alarms on which it is unknown when the activation condition will be fulfilled. Moreover, the followed approach for building the system makes it flexible and extensible for other predictive analysis tasks. The system has shown a great performance to predict three different types of alarms.
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Notes
Although this alarm type has been associated to all the resistors or thermocouple cables from the different zones of the extruder, only the one associated to the second zone of the die has been considered, because in the selected period of time is the only one that has been triggered.
Machine types in Google Compute Engine: https://cloud.google.com/compute/docs/machine-types.
GPU types in Google AI Platform: https://cloud.google.com/ml-engine/docs/using-gpus.
A model that uses the dependent relationship between an observation and some number of lagged observations.
The differencing of raw observations in order to make the time series stationary.
A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.
A Bayesian Optimization tool for black-box functions that allows tuning automatically machine learning models’ parameters.
Code and data for the research paper “Towards Open Set Deep Networks” (Bendale and Boult 2016) https://github.com/abhijitbendale/OSDN and an example of its implementation with Keras https://github.com/aadeshnpn/OSDN.
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Acknowledgements
This work is supported by the Spanish Ministry of Economy and Competitiveness (MEC) under Grant No.: FEDER/TIN2016-78011-C4-2-R. The work of Kevin Villalobos is funded by the Basque Government under Grant No.: PRE_2018_2_0263. Johan Suykens acknowledges support by ERC Advanced Grant E-DUALITY (787960), KU Leuven C14/18/068, FWO Project GOA4917N and Flemish Government Onderzoeksprogramma Artificiële Intelligentie Vlaanderen programme.
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Villalobos, K., Suykens, J. & Illarramendi, A. A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach. J Intell Manuf 32, 1323–1344 (2021). https://doi.org/10.1007/s10845-020-01614-w
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DOI: https://doi.org/10.1007/s10845-020-01614-w